TP53 mutation prevalence in normal airway epithelium as a biomarker for lung cancer risk

Abstract

Background There is a need for biomarkers that improve accuracy compared with current demographic risk indices to detect individuals at the highest lung cancer risk. Improved risk determination will enable more effective lung cancer screening and better stratification of lung nodules into high or low-risk category. We previously reported discovery of a biomarker for lung cancer risk characterized by increased prevalence of TP53 somatic mutations in airway epithelial cells (AEC). Here we present results from a validation study in an independent retrospective case–control cohort. Methods Targeted next generation sequencing was used to identify mutations within three TP53 exons spanning 193 base pairs in AEC genomic DNA. Results TP53 mutation prevalence was associated with cancer status (P < 0.001). The lung cancer detection receiver operator characteristic (ROC) area under the curve (AUC) for the TP53 biomarker was 0.845 (95% confidence limits 0.749–0.942). In contrast, TP53 mutation prevalence was not significantly associated with age or smoking pack-years. The combination of TP53 mutation prevalence with PLCOM2012 risk score had an ROC AUC of 0.916 (0.846–0.986) and this was significantly higher than that for either factor alone (P < 0.03). Conclusions These results support the validity of the TP53 mutation prevalence biomarker and justify taking additional steps to assess this biomarker in AEC specimens from a prospective cohort and in matched nasal brushing specimens as a potential non-invasive surrogate specimen.

DDR1 regulates RUNX1-CBFβ to control breast stem cell differentiation

“…5C). Notably, these are key TFs that regulate stemness and differentiation programs, in the breast but also male and female reproductive tissues (Chang et al, 2013; Hu et al, 2011; Huang et al; Jorgez et al, 2021; Murashima et al, 2015; Xu et al, 2023) stratified epithelial tissues (Barbieri and Pietenpol, 2006; Craig et al, 2023; Fang et al, 2020; Portal et al, 2022; Wu et al, 2003), and the hematopoietic system (Delgado and León, 2010; Huang et al, 2014; Kortylewski et al, 2005; Pant et al, 2012; Shimizu and Yamamoto, 2023; Trikha et al, 2011). Input of these transcription factor hits into STRINGDB, a database and visualizer of known protein-protein interactions (Szklarczyk et al, 2022) shows that these proteins form a closely related network, with each of these proteins only being 1-2 interactions away from each other (Supplemental Fig.…”

“…5C). Notably, these are key TFs that regulate stemness and differentiation programs, in the breast but also male and female reproductive tissues (Chang et al, 2013;Hu et al, 2011;Jorgez et al, 2021;Murashima et al, 2015;Xu et al, 2023) stratified epithelial tissues (Barbieri and Pietenpol, 2006;Craig et al, 2023;Fang et al, 2020;Portal et al, 2022;Wu et al, 2003), and the hematopoietic system (Delgado and León, 2010; (which was not certified by peer review) is the author/funder. All rights reserved.…”